Experts in the machine learning field typically create deep neural networks by hand and pick hyper-parameters through a trial-and-error process. As a result, the process becomes tiring and tedious, as well as prone to errors. A different perspective sees model design as a decision-making process that can be improved. We can automatically find the best combination of algorithms to maximize the performance of a task. With the growing interest in deep learning, AutoML [1]}, [2]} and Neural architecture search [3]} emerged where the entire DL pipeline could be automated, with the expectation to reduce the overall development cost and approach experts performances. There has been several efforts [4]}, [5]}, [6]}, [7]}, tracing back to the 90's [8]}, [9]}, formulating NAS as an optimization in the space of network architectures and solved using either reinforcement learning algorithms [7]}, [11]}, gradient-based optimization [12]} or sequential model-based optimization[11]}, [14]}.
